Krembil Research Institute, Toronto Western Hospital, University Health Network; Department of Laboratory Medicine and Pathobiology, University of Toronto;
Krembil Research Institute, Toronto Western Hospital, University Health Network; Department of Laboratory Medicine and Pathobiology, University of Toronto.
J Vis Exp. 2021 Feb 2(168). doi: 10.3791/62062.
Estimation of the number of dopaminergic neurons in the substantia nigra is a key method in pre-clinical Parkinson's disease research. Currently, unbiased stereological counting is the standard for quantification of these cells, but it remains a laborious and time-consuming process, which may not be feasible for all projects. Here, we describe the use of an image analysis platform, which can accurately estimate the quantity of labeled cells in a pre-defined region of interest. We describe a step-by-step protocol for this method of analysis in rat brain and demonstrate it can identify a significant reduction in tyrosine hydroxylase positive neurons due to expression of mutant α-synuclein in the substantia nigra. We validated this methodology by comparing with results obtained by unbiased stereology. Taken together, this method provides a time-efficient and accurate process for detecting changes in dopaminergic neuron number, and thus is suitable for efficient determination of the effect of interventions on cell survival.
估算黑质内多巴胺能神经元的数量是临床前帕金森病研究的关键方法。目前，无偏立体学计数是量化这些细胞的标准方法，但它仍然是一个繁琐且耗时的过程，对于所有项目来说可能都不太可行。在这里，我们描述了使用图像分析平台来准确估计预定义感兴趣区域内标记细胞数量的方法。我们描述了一种在大鼠脑内进行这种分析方法的分步方案，并证明它可以识别由于突变型α-突触核蛋白在黑质内的表达而导致酪氨酸羟化酶阳性神经元的显著减少。我们通过与无偏立体学获得的结果进行比较来验证这种方法学。总之，这种方法为检测多巴胺能神经元数量的变化提供了一种高效、准确的过程，因此适用于有效确定干预措施对细胞存活的影响。
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